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Record W4321018144 · doi:10.1161/hcv.0000000000000088

2023 ACC/AHA/SCAI Advanced Training Statement on Interventional Cardiology (Coronary, Peripheral Vascular, and Structural Heart Interventions): A Report of the ACC Competency Management Committee

2023· review· en· W4321018144 on OpenAlexaff
Theodore A. Bass, J. Dawn Abbott, Ehtisham Mahmud, Sahil A. Parikh, Jamil Aboulhosn, Mahi L. Ashwath, Bryan Baranowski, Lisa Bergersen, Hannah Chaudry, Megan Coylewright, Ali E. Denktas, Kamal Gupta, J. Antonio Gutierrez, Jonathan W. Haft, Beau M. Hawkins, Howard C. Herrmann, Navin K. Kapur, Sena Kiliç, John R. Lesser, Lin C. Huie, Rodrigo Mendirichaga, Vuyisile T. Nkomo, Linda G. Park, Dawn R. Phoubandith, Nishath Quader, Michael W. Rich, Kenneth Rosenfield, Saher S. Sabri, Murray L. Shames, Stanton K. Shernan, Kimberly A. Skelding, Jacqueline Tamis‐Holland, Vinod H. Thourani, Jennifer A. Tremmel, Seth Uretsky, Jessica Wageman, Frederick G.P. Welt, Brian Whisenant, Christopher J. White, Celina M. Yong

Bibliographic record

VenueCirculation Cardiovascular Interventions · 2023
Typereview
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsKimberly-Clark (Canada)
Fundersnot available
KeywordsMedicinePsychological interventionCardiologyInterventional cardiologyStatement (logic)Internal medicineNursing

Abstract

fetched live from OpenAlex

Angiography and Interventions representative.†Heart Rhythm Society representative.‡American Heart Association representative.§Society for Vascular Medicine representative.∥American Association for Thoracic Surgery representative.¶Heart Failure Society of America representative.#Society of Cardiovascular Computed Tomography representative.**American Society of Echocardiography representative.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.006
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0160.009

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.126
GPT teacher head0.385
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations36
Published2023
Admission routes1
Has abstractyes

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